- Team member EDUARD GIMENEZ
- Team member PATRICK ALTMEYER
- Team member SIMON NEUMEYER
- Team member JAKOB PÖRSCHMANN
11/28/2020
classifier.fit(X,y) in parallel for all \(m \in [1,24]\):\[ \begin{aligned} &&v_m&=\text{model}(\mathbf{X}_m) \\ \text{where}&& \mathbf{X}_m &= \begin{pmatrix} \mathbf{C}_1 & v_{1,-137} & ... & v_{1,m-1} \\ ... & ... & ... & ... \\ \mathbf{C}_{n_m} & v_{n_m,-137} & ... & v_{n_m,m-1} \end{pmatrix} \end{aligned} \]
classifier.predict(X) recursively for all \(m \in [1,24]\) where\[ \begin{aligned} && \mathbf{X}_m = \begin{pmatrix} \mathbf{C}_1 & v_{1,-137} & ... & \hat{v}_{1,m-1} \\ ... & ... & ... & ... \\ \mathbf{C}_{n_m} & v_{n_m,-137} & ... & \hat{v}_{n_m,m-1} \end{pmatrix} \end{aligned} \]
L = sc.betaincinv(alpha, beta, .075) U = sc.betaincinv(alpha, beta, .925)
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